Transformer-based based methods have had a significant impact on WSOL literature. However, they accumulate all attention maps from each layer, leading to a noisy CAM, or a suppression of different regions within the object [1]} (see Fig.REF ). Also, most techniques provide a heat map without sharp boundaries. In contrast, our proposed approach can produce high-resolution maps with sharper boundaries, only using attention from the last layer of the transformer block. Methods based on pseudo-labels fix their map for all epochs [2]}, generating a map very close to the pseudo-labels. Our method samples only a few pixels per epoch to learn foreground and background maps that continue to evolve based on a bi-nominal distribution. Sampling only a few pixels as pseudo-labels helps the network to explore different parts of the object, which leads to coverage of the whole object with sharper boundaries. Since these methods are typically computationally intensive during training, we harvest maps in a self-supervised way using a pretrained transformer, allowing to obtain clues about potential objects and train our localization network. Our method leverages long-range dependencies as pseudo-labels are harvested from a transformer, and convolution inductive bias to suppress background noise.
